Unified influence estimation through multi-hop reinforcement and diversity-weighted fusion in weighted complex networks

Abstract By depicting items as nodes and their connections as links, networks or graphs attract more attention in complex systems as a way to simulate real-world interactions. Finding influential nodes in human contact networks or other social networks is essential to comprehending disease transmission, which is dependent on the frequency and intensity of contact. Nevertheless, the majority of current research ignores the variability of real-world interactions in favour of uniform connection strength. To address this gap, we propose a unified influence estimation model (uiem) that integrates multi-hop diffusion, local structural reinforcement, and interaction diversity into a single adaptive framework. The model constructs a weighted graph where edge weights reflect interaction frequency/ real weights based on the different scenarios. One-hop and two-hop components capture direct and indirect diffusion influence, while the local structural reinforcement index (lsri) quantifies a node’s connectivity strength and connections within its neighborhood. Additionally, a diversity-weighted fusion (dwf) mechanism combines weighted degree and local clustering entropy (lce) to balance structural intensity and interaction diversity. Experimental results on multiple human contact networks and social networks demonstrate that uiem outperforms existing methods, effectively identifying structurally and functionally influential nodes and providing deeper insights into the dynamics of real-world contact-based spreading processes.